SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He
zhen@nus.edu.sg,luo.bingqiao@u.nus.edu,lusl@nus.edu.sg,hebs@comp.nus.edu.sg
Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of Live Graph Lab for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. Among them, Non-fungible tokens (NFTs) have become one of the most prominent parts of blockchain over the past several years. With more than $40 billion market capitalization, this decentralized ecosystem produces massive, anonymous and real transaction activities, which naturally forms a complicated transaction network. However, there is limited understanding about the characteristics of this emerging NFT ecosystem from a temporal graph analysis perspective. To mitigate this gap, we instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights. Specifically, through downloading and parsing the NFT transaction activities, we obtain a temporal graph with more than 4.5 million nodes and 124 million edges. Then, a series of measurements are presented to understand the properties of the NFT ecosystem. Through comparisons with social, citation, and web networks, our analyses give intriguing findings and point out potential directions for future exploration. Finally, we also study machine learning models in this live graph to enrich the current datasets and provide new opportunities for the graph community. The source codes and dataset are available at https://livegraphlab.github.io.
Temporal graphs provide an accurate representation of real-world systems, including social networks, transaction networks and the Web [5, 38, 39, 41], etc. By investigating temporal graphs, we can gain insights into the temporal dynamics and understand how these systems evolve and function [86, 6]. Notably, a growing number of graph mining algorithms [36, 25, 85] and graph systems [69, 35, 13] have been developed. However, with this continuously growing trend, several severe issues emerge, which limit the further development of graph community. In the current literature, studies are usually conducted on a set of outdated and incomplete graphs. The majority of the aforementioned graphs are either not easily available or their graph structures are incomplete since they cannot record all the interactions in graph. Moreover, even through all the interactions are recorded, they might not be shareable in a public and timely evolving manner, such as social networks in companies like Meta and Tencent. Thus, for meaningful temporal graph analysis and benchmarks, we need open graph datasets that evolve dynamically and are easily accessible in a timely manner.
To bridge this gap, we propose the concept of Live Graph Lab, which provides live graphs according to blockchain transactions. Specifically, we offer a set of tools for downloading, parsing, cleaning, and analyzing blockchain transactions to empower the analyses of transaction graphs. It not only alleviates
Table 1: Comparisons among different types of graph datasets.
Categories	Datasets	Open	Timely Evolving	Complete Structure	Timestamp
Social Network	ego-Twitter [45]	✓	✗	✗	✗
Citation Network	DBLP [70]	✓	✗	✗	✓
The Web	web-Google [44]	✓	✗	✗	✗
Blockchain	Live Graph Lab	✓	✓	✓	✓
the researchers' burden of accessing massive raw transaction data, but also brings a considerable of opportunities to conduct experiments in the real-world scenario for temporal graph studies. Our introduced live graphs have several unique characters like open availability, dynamic evolution and real transactions due to the inherent characteristics of the decentralized blockchain. Therefore, it is of great importance to investigate the properties of these live graphs to provide new insights for graph algorithms and systems.
Today, as blockchain technology becomes more widespread, the token economy is gradually emerging. Non-fungible tokens (NFTs) have seen tremendous growth with its market capitalization reaching over $40 billion. Notably, one of the digital work named "Everydays: The First 5000 Days" by artist Beeple was sold for $69 million, which makes NFTs become the center of attention. This phenomenon also leads to an increasing number of enthusiasts participating in this emerging concept. At the same times, it generates massive, anonymous and real transaction activities, which naturally forms a complex NFT transaction network. Although traditional networks like social networks and citation networks have been extensively studied, they are often outdated due to their lack of constantly updating. To enrich the current datasets and overcome their limitations, we instantiate a live graph with NFT transaction network by synchronizing a full Ethereum node, thus it continuously keeps up with the latest Ethereum block and includes all the transaction data. To investigate the characteristics of this live NFT transaction network, we present a temporal graph extracted from a specific time period spanning from 2017 to 2022, which comprises over 4.5 million nodes and 124 million edges. Then, comprehensive analyses are performed, and the results demonstrate that our presented live graph exhibits a variety of characteristics, offering exciting opportunities for the graph community. To summarize, the main contributions and findings are as follows:
- We introduce the concept of live graph lab, which focuses on open, dynamic and real graphs.
- We instantiate a live graph with NFT transaction network and provide a systemic analysis, which demonstrates interesting properties like fast-growing, highly-active, ect.
- Graph machine learning models are investigated in the live graph, and the experimental results indicate that live graphs pose new challenges and opportunities for the graph community.
Graph has gained significant attention from both academic and industrial communities. A wide range of benchmark datasets have been proposed to facilitate the research in graph community. Among them, SNAP [43] and Network Data Repository [60] provide diverse types of graphs including social networks, web networks, etc. AMiner [70] offers comprehensive citation networks extracted from DBLP, ACM and Microsoft Academic Graph. Chartalist [63] presents a set of blockchain datasets to enable machine learning model development. Although these datasets are publicly available, they are not constantly updated in a nearly real-time manner. Take social network as an example, the ego-Twitter [45] in SNAP project was released more than 10 years ago. Thus, the graph properties presented in these benchmarks may no longer be suitable in the current context. Meanwhile, their graph structures are usually incomplete due to privacy policy constraints or technical limitations. However, these characters are important for various downstream applications. For instance, if the graph is incomplete or their characteristics have changed significantly, the learning outcomes could be ineffective and even misleading in the graph learning tasks. To enrich the current datasets and overcome their limitations, we propose the concept of Live Graph Lab, which supports various experiments for temporal graph algorithms. We provide a detailed comparison of these datasets in Table 1. Specifically, the proposed live graph lab has the following properties: (1) it is open and
<sup>1</sup>https://en.wikipedia.org/wiki/Everydays:_the_First_5000_Days
Table 2: Statistics of the dataset.
Descriptions	Statistics
Start date (mm-dd-yyyy, UTC)	07-12-2017 13:49
End date (mm-dd-yyyy, UTC)	08-01-2022 06:50
Number of NFT collections	97,667
Number of NFT tokens	77,991,885
Number of account addresses	4,531,020
Number of transactions	124,660,813
publicly available; (2) it is constantly evolving in a nearly real-time manner; (3) it is complete (i.e, all interactions are fully recorded); (4) it has realistic timestamp. Moreover, previous blockchain datasets mainly focus on fungible token transactions. However, Non-Fungible Tokens (NFTs), which are a vital component of the Ethereum, have been overlooked by existing works. Our work covers this gap by delving into this emerging NFT ecosystem.
3 Dataset Details
In this paper, we instantiate a live graph with NFT transaction network in the Ethereum blockchain. We first provide an overview of the blockchain background. Then, we give a comprehensive explanation of the graph construction process. Note that, our methodologies are applicable to other bolckchains like Solana and Polygon, etc.
Blockchain and Ethereum. Blockchain, a distributed ledger technology, has attracted continuous attention recent years and is made up of securely linked blocks with cryptography techniques [53], where each block contains information of the previous block (e.g., cryptographic hash). Ethereum is a decentralized, programmable blockchain, which means users can construct various decentralized applications on the blockchain. Ether (ETH) is the native cryptocurrency of Ethereum, and every transaction incurred in the Ethereum needs a specific fee paid in ETH.
Smart Contract and Non-Fungible Token. Smart contract is an important feature in the Ethereum blockchain [88], which is a computer program that runs on the Ethereum to automatically execute or control relevant events and actions according to its logic. Smart contracts have largely reduced the requirements for trusted intermediaries, fraud losses and arbitration costs, etc. Non-Fungible Token (NFT) is one of the most successful applications on Ethereum. NFTs are tokens that can be used to represent the ownership of any unique asset such as image, video and audio, etc. Different from fungible items where one dollar is exchangeable for another one dollar, NFTs are not interchangeable with each other, since they all have unique properties and are not divisible.
3.2 Raw Data
The statistic information of our dataset is summarized in Table 2. To access the transaction information in Ethereum, Geth, a golang implementation of Ethereum client software, is launched to facilitate the synchronization of the ledger on Ethereum mainnet. When the client synchronizes to the latest block, we extract all the blocks before Aug 1st, 2022 (i.e., from block #0 to block #15,255,104). Then, we parse all the transaction data and log data via toolkit Ethereum ETL<sup>2</sup>. Thanks to the well-defined standards in NFT communities, it is convenient for us to extract the information we need. Specifically, according to the standard of EIP-721, every NFT smart contract must implement the standard interfaces including transfer, approval, ownerof and balanceOf, etc. For those smart contracts that do not strictly follow the EIP-721 standard, we remove those smart contracts and its relevant transactions from our data. For the remaining data, we filter out all the transfer events triggered by its smart contracts to extract the NFT transactions. This is because the ownership change of any NFT will emit a transfer event identified by the event topic Keccack256 hash  $0xdff\ldots3ef$ . In the NFT transfer event, it contains four key contents including the Keccack256 hash, the sender's address, the receiver's address, and the transferred NFT token ID. The time when the transaction occurs can be
$^{2}$ https://ethereum-etl.readthedocs.io/en/latest/
(a) Nodes
(b) Edges
(c) Edge percentages
(d) Degree distribution
Figure 1: Evolution of nodes and edges.
retrieved from the block that the event was found. Once we have obtained all of these key information, we can know when and which NFTs are transferred among different wallet addresses and the prices they are sold.
By parsing the transfer events, we find out that there are 97,667 NFT collections with 77,991,885 NFT tokens, where each collection contains different numbers of NFT tokens (e.g., varying from thousands to millions). Meanwhile, 124,660,813 transactions are extracted, and among them 4,531,020 users (i.e., we regard each wallet address as a user) participate in the transactions. It is worthy noting that there are over 100,000 NFT collections according to Etherscan<sup>3</sup>. However, as we have mentioned, some of them are not standard NFT tokens (i.e., they do not strictly follow the EIP-721 standard), and we remove them from our data. Thus, our method almost extracts all the NFT collections.
3.3 Graph Construction
We investigate the structure and dynamics of all these transactions by constructing a directed temporal graph  $\mathcal{G} = (\mathcal{N},\mathcal{E},\mathcal{T})$ , where  $\mathcal{N}$  and  $\mathcal{E}$  denote the node set and edge set, respectively. That's to say, we only count once for those addresses that have repetitive interactions.  $\mathcal{T}$  is a set of timestamps when the interactions happen. We use  $t(e)$  to denote the timestamp when edge  $e$  is formed, and  $t(u)$  represents when the node  $u$  is added into the graph. Thus,  $a_{t}(u) = t - t(u)$  reflects node  $u$ 's age at time  $t$ . For any given time  $t$ , graph  $\mathcal{G}_t$  consists of all the nodes as well as edges until time  $t$ . Note that, since we have accurate timestamps for the arrival of each node and edge, our investigation of graph dynamics is at a much finer granularity compared with the majority of existing studies [5, 41].
4 Observations and Analyses
To have a better understanding of the instantiated live graph, we start the analyses from the following perspectives: (1) Structural properties, which investigate how its nodes and edges change as time goes on; (2) Dynamic behaviors, which are graph specific properties such as how hub-nodes and bi-directional edges are formed. More comprehensive analyses are given in the Appendix C and D.
4.1 Structural Properties
To fully understand how active is the NFT transaction network, we measure the evolution of nodes and edges over the time. Specifically, we set the time granularity to a yearly yardstick. Since our data started from the July of 2017 and ended at the August of 2022, the statistical information for 2017 and 2022 only has a half year's data. Figure 1a and 1b show the annual growth of nodes and edges in a log-scale. As observed, there is a rapid increase in the number of nodes and edges, which become  $28\mathrm{K}$  and  $165\mathrm{K}$  times larger within the six years. It means the NFT transaction network is highly active and growing at a fast speed.
To further figure out what leads to the growth, we analyse the newly added nodes in each pair of consecutive years. New nodes could join the network through different ways (i.e., mint, buy or airdrop NFTs). Among them, mint is the most common and easy way to obtain NFTs, where only a small number of gas fee is needed to pay. Figure 1a presents the trend of newly added mint nodes and non-mint nodes. We notice that the number of mint nodes added into the network is approximately the same magnitude of the total nodes at that time. Therefore, mint NFTs dominates the growth of
<sup>3</sup>https://etherscan.io/tokens-nft
(a) Hub nodes in year 2018
(b) Hub nodes in year 2019
Figure 2: Correlation between hub nodes, new connections and mutual edges.
(c) Mutual edges in days
(d) Ratio of mutual edges
nodes in the network at present, meanwhile the number of non-mint nodes are also increasing at a high velocity. These two phenomena result in the expansion of nodes.
Figure 1b also shows the trend of added bidirectional edges and self-edges. We can see that the number of bidirectional edges only accounts for a small volume. Unlike social networks where the edges are highly mutual, the NFT market is an anonymous ecosystem and the probability of mutually interacting with each other is relatively low. These bidirectional edges might happen in the scenarios of swap or wash trading activities [73]. We also notice that there exist a very few self-loops in the network, which means these addresses transact with themselves. This abnormal phenomenon might be caused by a mistake input for the received address or these transactions are executed for testing. Furthermore, to characterize how active are these addresses, we compute the percentage of edges in which new nodes have links from or to old nodes, and the percentage of edges which only contain links between new nodes. Here, we refer to new nodes as the nodes created in the current year, and old nodes indicate the nodes that have existed in the previous years. Figure 1c demonstrates that more than  $50\%$  percent of newly added edges are constituted by the connections between new nodes and old nodes, except for the year of 2021. This indicates that most of the addresses remain active as the NFT ecosystem become mature. It is also very interesting to note that the newly created addresses are highly active in 2021, and at the same time, the whole NFT market capitalization reaches over $40 billion USD dollars in this year.
For completeness, we also show the degree distribution of the NFT transaction network in Figure 1d with log-log scale. As expected, it follows a power-law distribution. We observe that some nodes have the degree of 1, which indicates they did not conduct any other transactions after the NFTs were minted or transferred. There also exist several high degree hubs that are extremely active and interacts with more than thousands of different addresses. Among them, a special Null address (i.e.,  $0x000\dots 000)^4$  has the highest degree, since every NFT mint activity will create a link from the Null address to the mint address.
4.2 Dynamic Behaviors
Evolution of Hub Nodes. The node degree shows a heavily long-tailed distribution (i.e., Figure 1d), and the assortativity raises year by year (i.e., Figure 4a in Appendix C). Both these two measurements are highly relevant to the evolving of hub nodes in the transaction network. Figure 2a and 2b illustrate the correlations between node degrees and its number of new connections in two consecutive years. We use blue color to present the node degree distribution in the previous year, and then red color indicates the number of connections from new nodes in the current year. As expected, we can find that if a node had high degree in the previous year, it would have high probability to get more new node connections in the current year. Moreover, this observation is also validated by measuring their Pearson Correlation Coefficients. We evaluate the correlation coefficients between node degrees and its number of new node connections (in year 2018-2019 and 2019-2020), which are 0.2266 and 0.5476, respectively. The results reveal that these two factors are positively relevant. Thus, we can draw the conclusion that the NFT transaction network follows preferential attachment growth model [54, 32], i.e., "the rich gets richer".
Next, we analyze the distribution of the hub nodes. Specifically, we note that half of the top-100 largest hub nodes are smart contract addresses. This is understandable, since smart contracts play an important role in providing various services (e.g., NFT fractionalization and staking) to other users
$40\mathrm{x}0000000000000000000000000000000000000000$
in the network. Thus, they tend to have high degrees once they are frequently used by others. The transactions with such smart contracts result in the increase of assortativity. Furthermore, there also exist some hub nodes that are not smart contracts. For instance, we observe that address  $0x8a0\dots 700^5$  is the fifth largest hub nodes with the degree of 69,458. After checking its transactions manually, we find that all its transactions are about HyperNFT tokens, and it holds more than 180,000 such kind of tokens. Since it is strange, we then check its transactions in details and discover that this address creates a transaction every a few minutes, where the ID of the transacted NFT token is in a continuous and increase order. According to these clues, it is very certain that this address is a bot, which makes frequent transactions to pretend it's very prosperous to attract more users to join in. The hub nodes are responsible for the spreading of information in the network, thus it is necessary to investigate the properties of hub nodes.
Mutual Interaction Edges. In the previous study, we find that the reciprocity of the transaction network is relatively low, which is around 0.1 in the end (i.e., Figure 4c in Appendix C). This is quite different from the property in the social networks, where the reciprocity is very high and the value is above 0.7 [39]. One possible reason is that people are prone to mutually following and interacting with their friends in the social networks, since it does not cost anything. However, it becomes different in the NFT transaction network, because we do not know who we are actually interacting with and every action costs in the blockchain. To uncover the reciprocity's characteristics, we focus on the mutual interaction edges. Specifically, we are interested in if two nodes are mutually interacting with each other, i.e., reciprocal edges  $\langle u,v\rangle_t$  and  $\langle v,u\rangle_{\hat{t}}$ , what their maximum time interval distribution  $|t - \hat{t}|$  looks like, i.e., the maximum delay in days of the reciprocity. Figure 2c shows that mutual interaction edges forming within one day account for the highest proportion, and the maximum time interval can reach to more than 1,000 days. According to Figure 2d, we can observe that about  $15\%$  of the reciprocal edges are formed almost simultaneously among those bi-directional edges, and more than  $70\%$  of them are formed within 90 days. From these observations, we can conclude that the NFT transaction network can not be regarded as an undirected network due to its low reciprocity value, and the simultaneously formed bi-directional edges can be a good indicator to judge the abnormal activities. For instance, it may be caused by the token transfers among the accounts controlled by a same person.
To further investigate this phenomenon, we want to know how many address pairs are suspicious in these bi-directional links. We first conduct some statistics for all NFT transaction addresses, which involves the following two factors: the number of transactions (including from transactions and to transactions) and the number of distinctly interacted addresses. Then, if one address has very limited transactions and it only interacts with a specific address, it is highly possible that these two addresses are of same person's wallets. This is because this address is actually not active, but it responses promptly in the bi-directional links. Similarly, another situation is although one address has a lot of transactions, it interacts with one specific address frequently and the specific address accounts for a large ratio of the total transactions. They may also belong to same person's wallets. Based on these two observations, we define the following two rules to identify the suspicious address pairs: 1) one of them makes less than 5 transactions or 2) the transaction ratios between them is larger than 0.8. If they satisfy at least one of the rules, we then believe these two addresses are likely to be same person's wallets. According to these rules, we discover that  $33.72\%$  of those simultaneously formed bi-directional links have high probability of being suspicious. This observation provides a new perspective for detecting anomaly transactions.
5 Downstream Applications
The above analyses provide a general overview of this live graph's properties. Our results demonstrate that the transaction network is highly active and evolving at a fast speed. These properties put forward new challenges for various downstream tasks. Next, we investigate three widely studied tasks.
5.1 Temporal Link Prediction
Link prediction lies at the core of graph analysis and mining. The link prediction's goal is to predict whether a pair of node would form a link, which has been extensively used in diverse real-world scenarios like recommendation [79, 7] and knowledge graph completion [55], just to name a few. In
$50x8a01fa5a77311bbcf29e293d8ecb48707cfdb700$